The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a veryâ€¦ (More)

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions,â€¦ (More)

Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In theâ€¦ (More)

A new regression technique based on Vapnikâ€™s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regressionâ€¦ (More)

The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error ofâ€¦ (More)

We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for theâ€¦ (More)